How to design a dose-finding study using the continual reassessment method

The 3 + 3 design in dose-finding studies with small sample sizes: Pitfalls and possible remedies

In the last few years, numerous novel designs have been proposed to improve the efficiency and accuracy of phase I trials to identify the maximum-tolerated dose (MTD) or the optimal biological dose (OBD) for noncytotoxic agents. However, the conventional 3+3 approach, known for its and poor performance, continues to be an attractive choice for many trials despite these alternative suggestions. The article seeks to underscore the importance of moving beyond the 3+3 design by highlighting a different key element in trial design: the estimation of sample size and its crucial role in predicting toxicity and determining the MTD. We use simulation studies to compare the performance of the most used phase I approaches: 3+3, Continual Reassessment Method (CRM), Keyboard and Bayesian Optimal Interval (BOIN) designs regarding three key operating characteristics: the percentage of correct selection of the true MTD, the average number of patients allocated per dose level, and the average total sample size. The simulation results consistently show that the 3+3 algorithm underperforms in comparison to model-based and model-assisted designs across all scenarios and metrics. The 3+3 method yields significantly lower (up to three times) probabilities in identifying the correct MTD, often selecting doses one or even two levels below the actual MTD. The 3+3 design allocates significantly fewer patients at the true MTD, assigns higher numbers to lower dose levels, and rarely explores doses above the target dose-limiting toxicity (DLT) rate. The overall performance of the 3+3 method is suboptimal, with a high level of unexplained uncertainty and significant implications for accurately determining the MTD. While the primary focus of the article is to demonstrate the limitations of the 3+3 algorithm, the question remains about the preferred alternative approach. The intention is not to definitively recommend one model-based or model-assisted method over others, as their performance can vary based on parameters and model specifications. However, the presented results indicate that the CRM, Keyboard, and BOIN designs consistently outperform the 3+3 and offer improved efficiency and precision in determining the MTD, which is crucial in early-phase clinical trials.

Protocol for the challenge non-typhoidal Salmonella (CHANTS) study: a first-in-human, in-patient, double-blind, randomised, safety and dose-escalation controlled human infection model in the UK

INTRODUCTION

Invasive non-typhoidal Salmonella (iNTS) serovars are a major cause of community-acquired bloodstream infections in sub-Saharan Africa (SSA). In this setting, Salmonella enterica serovar Typhimurium accounts for two-thirds of infections and is associated with an estimated case fatality rate of 15%-20%. Several iNTS vaccine candidates are in early-stage assessment which-if found effective-would provide a valuable public health tool to reduce iNTS disease burden. The CHANTS study aims to develop a first-in-human Salmonella Typhimurium controlled human infection model, which can act as a platform for future vaccine evaluation, in addition to providing novel insights into iNTS disease pathogenesis.

METHODS AND ANALYSIS

This double-blind, safety and dose-escalation study will randomise 40-80 healthy UK participants aged 18-50 to receive oral challenge with one of two strains of S. Typhimurium belonging to the ST19 (strain 4/74) or ST313 (strain D23580) lineages. 4/74 is a global strain often associated with diarrhoeal illness predominantly in high-income settings, while D23580 is an archetypal strain representing invasive disease-causing isolates found in SSA. The primary objective is to determine the minimum infectious dose (colony-forming unit) required for 60%-75% of participants to develop clinical or microbiological features of systemic salmonellosis. Secondary endpoints are to describe and compare the clinical, microbiological and immunological responses following challenge. Dose escalation or de-escalation will be undertaken by continual-reassessment methodology and limited within prespecified safety thresholds. Exploratory objectives are to describe mechanisms of iNTS virulence, identify putative immune correlates of protection and describe host-pathogen interactions in response to infection.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from the NHS Health Research Authority (London-Fulham Research Ethics Committee 21/PR/0051; IRAS Project ID 301659). The study findings will be disseminated in international peer-reviewed journals and presented at national/international stakeholder meetings. Study outcome summaries will be provided to both funders and participants.

TRIAL REGISTRATION NUMBER

NCT05870150.

Handling Incomplete or Late-Onset Toxicities in Early-Phase Dose-Finding Clinical Trials: Current Practice and Future Prospects

PURPOSE

The way late-onset toxicities are managed can affect trial outcomes and participant safety. Specifically, participants often might not have completed their entire follow-up period to observe any toxicities before new participants would be recruited. We conducted a methodological review of published early-phase dose-finding clinical trials that used designs accounting for partial and complete toxicity information, aiming to understand (1) how such designs were implemented and reported and (2) if sufficient information was provided to enable the replicability of trial results.

METHODS

Until March 26, 2023, we identified 141 trials using the rolling 6 design, the time-to-event continuous reassessment method (TITE-CRM), the TITE-CRM with cycle information, the TITE Bayesian optimal interval design, the TITE cumulative cohort design, and the rapid enrollment design. Clinical settings, design parameters, practical considerations, and dose-limiting toxicity (DLT) information were extracted from these published trials.

RESULTS

The TITE-CRM (61, 43.3%) and the rolling 6 design (76, 53.9%) were most frequently implemented in practice. Trials using the TITE-CRM had longer DLT assessment windows beyond the first cycle compared with the rolling 6 design (52.5% v 6.6%). Most trials implementing the TITE-CRM (91.8%, 56 of 61) failed to describe essential parameters in the protocols or the study result papers. Only five TITE-CRM trials (8.2%, 5 of 61) reported sufficient information to enable replication of the final analysis.

CONCLUSION

When compared with trials using the rolling 6 design, those implementing the TITE-CRM design exhibited notable deficiencies in reporting essential details necessary for reproducibility. Inadequate reporting quality of advanced model-based trial designs hinders their credibility. We provide recommendations that can improve transparency, reproducibility, and accurate interpretation of the results for such designs.

Adjusting perioperative methadone dose for elderly and fragile hip fracture patients (MetaHip-trial) - A statistical analysis plan for an adaptive dose-finding trial

Background

The elderly population is expanding globally. This gives numerous challenges especially regarding hip fracture patients. In the US alone over 300.000 hip fracture patients are treated each year, and a large amount of those develop opoid addiction. Hip fractures require surgical intervention within 24 h and is associated with significant pain even at rest. Postoperative analgesic treatment need to be optimized to ensure adequate pain relief and to prevent subsequent opioid addiction. Previous studies have shown that methadone effectively decreases post-operative opioid consumption but the studies focused on younger patients undergoing elective surgery. This study focus on the use of methadone on the elderly, fragile patients undergoing acute surgery, by first determining the maximal tolerable dose.The hypothesis is the maximal tolerable doses of these hip-fracture patients lies between 0.10 mg/kg and 0.20 mg/kg. This trial aims to estimate the maximum tolerable dose of methadone when administered to elderly patients undergoing surgery for a hip fracture.

Method

This project is an adaptive dose-finding trial. The continuous reassessment method will estimate the maximum tolerable dose of methadone. The primary outcome will be respiratory depression. The statistical analysis plan will be published a priori to the closure of patient recruitment and statistical analysis of database results.

Conclusion

The results of this study will give valuable information about the maximally tolerated dose of methadone for postoperative pain relief for elderly patients with hip fractures and potential adverse events.This trial is registered on clinicaltrials.gov with trial registration: NCT05581901. Registered 17 October 2022, https://www.clinicaltrials.gov/ct2/show/NCT05581901?term=methadone&cond = hip&draw = 2&rank = 1.

A flexible dose-response modeling framework based on continuous toxicity outcomes in phase I cancer clinical trials

BACKGROUND

The past few decades have seen remarkable developments in dose-finding designs for phase I cancer clinical trials. While many of these designs rely on a binary toxicity response, there is an increasing focus on leveraging continuous toxicity responses. A continuous toxicity response pertains to a quantitative measure represented by real numbers. A higher value corresponds not only to an elevated likelihood of side effects for patients but also to an increased probability of treatment efficacy. This relationship between toxicity and dose is often nonlinear, necessitating flexibility in the quest to find an optimal dose.

METHODS

A flexible, fully Bayesian dose-finding design is proposed to capitalize on continuous toxicity information, operating under the assumption that the true shape of the dose-toxicity curve is nonlinear.

RESULTS

We conduct simulations of clinical trials across varying scenarios of non-linearity to evaluate the operational characteristics of the proposed design. Additionally, we apply the proposed design to a real-world problem to determine an optimal dose for a molecularly targeted agent.

CONCLUSIONS

Phase I cancer clinical trials, designed within a fully Bayesian framework with the utilization of continuous toxicity outcomes, offer an alternative approach to finding an optimal dose, providing unique benefits compared to trials designed based on binary toxicity outcomes.

Novel Clinical Trial Designs with Dose Optimization to Improve Long-term Outcomes

Conventional designs for choosing a dose for a new therapy may select doses that are unsafe or ineffective and fail to optimize progression-free survival time, overall survival time, or response/remission duration. We explain and illustrate limitations of conventional dose-finding designs and make four recommendations to address these problems. When feasible, a dose-finding design should account for long-term outcomes, include screening rules that drop unsafe or ineffective doses, enroll an adequate sample size, and randomize patients among doses. As illustrations, we review three designs that include one or more of these features. The first illustration is a trial that randomized patients among two cell therapy doses and standard of care in a setting where it was assumed on biological grounds that dose toxicity and dose-response curves did not necessarily increase with cell dose. The second design generalizes phase I-II by first identifying a set of candidate doses, rather than one dose, randomizing additional patients among the candidates, and selecting an optimal dose to maximize progression-free survival over a longer follow-up period. The third design combines a phase I-II trial and a group sequential randomized phase III trial by using survival time data available after the first stage of phase III to reoptimize the dose selected in phase I-II. By incorporating one or more of the recommended features, these designs improve the likelihood that a selected dose or schedule will be optimal, and thus will benefit future patients and obtain regulatory approval.

Building an adaptive dose simulation framework to aid dose and schedule selection

Establishing a dosing regimen that maximizes clinical benefit and minimizes adverse effects for novel therapeutics is a key objective for drug developers. Finding an optimal dose and schedule can be particularly challenging for compounds with a narrow therapeutic window such as in oncology. Modeling and simulation tools can be valuable to conduct in silico evaluations of various dosing scenarios with the goal to identify those that could minimize toxicities, avoid unscheduled dose interruptions, or minimize premature discontinuations, which all could limit the potential for therapeutic benefit. In this tutorial, we present a stepwise development of an adaptive dose simulation framework that can be used for dose optimization simulations. The tutorial first describes the general workflow, followed by a technical description with basic to advanced practical examples of its implementation in mrgsolve and is concluded with examples on how to use this in decision-making around dose and schedule optimization. The adaptive simulation framework is built with pharmacokinetic, pharmacodynamic (i.e., biomarkers, activity markers, target engagement markers, efficacy markers), and safety models that include evaluations of unexplained interindividual and intraindividual variability and covariate impact, which can be replaced and expanded (e.g., combination setting, comparator setting) with user-defined models. Subsequent adaptive simulations allow investigation of the impact of starting dose, dosing intervals, and event-driven (exposure or effect) dose modifications on any end point. The resulting simulation-derived insights can be used in quantitatively proposing dose and regimens that better balance benefit and adverse effects for further evaluation, aiding dose selection discussions, and designing dose modification recommendations, among others.

Medicines and Healthcare products Regulatory Agency's "Consultation on proposals for legislative changes for clinical trials": a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing

In the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals "to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines". The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research. Two topics arose frequently in the discussion: the emphasis on legislation, and the absence of questions on data sharing. It is our opinion that the proposals rely heavily on legislation to change practice. However, clinical trials are heterogeneous, and as a result some trials will struggle to comply with all of the proposed legislation. Furthermore, adaptive design clinical trials are even more heterogeneous than their non-adaptive counterparts, and face more challenges. Consequently, it is possible that increased legislation could have a greater negative impact on adaptive designs than non-adaptive designs. Overall, we are sceptical that the introduction of legislation will achieve the desired outcomes, with some exceptions. Meanwhile the topic of data sharing - making anonymised individual-level clinical trial data available to other investigators for further use - is entirely absent from the proposals and the consultation in general. However, as an aspect of the wider concept of open science and reproducible research, data sharing is an increasingly important aspect of clinical trials. The benefits of data sharing include faster innovation, improved surveillance of drug safety and effectiveness and decreasing participant exposure to unnecessary risk. There are already a number of UK-focused documents that discuss and encourage data sharing, for example, the Concordat on Open Research Data and the Medical Research Council's Data Sharing Policy. We strongly suggest that data sharing should be the norm rather than the exception, and hope that the forthcoming proposals on clinical trials invite discussion on this important topic.

Tocilizumab in combination with a standard induction chemotherapy in acute myeloid leukaemia patients (TOCILAM study): a single-centre, single-arm, phase 1 trial

Background

In acute myeloid leukaemia (AML), interleukin-6 (IL-6) promotes chemo-resistance and its levels correlate with poor prognosis. IL-6 blockade may represent a promising therapeutic strategy. We aimed to test, tocilizumab, an anti-IL-6 receptor (R) monoclonal antibody in combination with standard intensive AML induction chemotherapy.

Methods

This investigator-initiated single-centre phase 1 trial was conducted at Nantes University Hospital in France. According to a continual reassessment method, three escalating doses were tested of intravenous (IV) tocilizumab (4, 6, and 8 mg/kg) administered at day (d) 8 of a standard AML induction chemotherapy (IV idarubicine 8 mg/m2 d1 to d5 + IV cytarabine 100 mg/m2 d1 to d7). All adults (aged ≥ 18 years) with an Eastern Cooperative Oncology Group performance status of 0-2 and with a newly diagnosed (excluding patients with a favourable risk according to ELN-2017 classification if <60 year-old) or a relapsed/refractory AML were eligible. The primary objective was to determine the maximum tolerated dose of tocilizumab to administrate with a standard intensive AML induction. Safety outcomes were continuously monitored for at each participant contact. This trial is registered with ClinicalTrials.gov, NCT04547062.

Findings

Between Dec 29, 2020 and Dec 1, 2022, 12 patients were enrolled, of whom 75% had an ELN-2017 high-risk profile, and were treated with tocilizumab- two patients at 4 mg/kg, two at 6 mg/kg and eight at 8 mg/kg of tocilizumab. No dose-limiting toxicity related to tocilizumab was documented. There were nine serious adverse events, none of which were related to tocilizumab, and there was no treatment-related deaths. MTD was thus not reached. Two deaths occurred during induction. In the remaining ten evaluable patients, nine responded to treatment.

Interpretation

The combination of tocilizumab with standard AML intensive induction appears to be safe and resulting responses are encouraging. A dose of 8 mg/kg of tocilizumab given at day 8 of induction could be used for further phase 2/3 studies.

Funding

The Leucémie Espoir Atlantique Famille (LEAF)-"Tous avec Fabien" association.

Considerations for Cell and Gene Therapy Programs Entering the Clinical Space

Cell and gene therapy (CGT) describes a broad category of medicinal products with potential applications to prevent and treat human disease in multiple therapeutic areas. These therapies leverage the use of modified nucleic acids, altered cells or tissue, or both. The modality, mechanism, route of administration, and therapeutic indication for a CGT product will influence the challenges and opportunities for early clinical development, some of which may be highly specific to the product under consideration. Both the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) encourage early interaction between sponsor and health authority to align on key elements of the CGT development program.

Early phase clinical trials in oncology: Realising the potential of seamless designs

BACKGROUND

The pharmaceutical industry's productivity has been declining over the last two decades and high attrition rates and reduced regulatory approvals are being seen. The development of oncology drugs is particularly challenging with low rates of approval for novel treatments when compared with other therapeutic areas. Reliably establishing the potential of novel treatment and the corresponding optimal dosage is a key component to ensure efficient overall development. A growing interest lies in terminating developments of poor treatments quickly while enabling accelerated development for highly promising interventions.

METHODS

One approach to reliably establish the optimal dosage and the potential of a novel treatment and thereby improve efficiency in the drug development pathway is the use of novel statistical designs that make efficient use of the data collected.

RESULTS

In this paper, we discuss different (seamless) strategies for early oncology development and illustrate their strengths and weaknesses through real trial examples. We provide some directions for good practices in early oncology development, discuss frequently seen missed opportunities for improved efficiency and some future opportunities that have yet to fully develop their potential in early oncology treatment development.

DISCUSSION

Modern methods for dose-finding have the potential to shorten and improve dose-finding and only small changes to current approaches are required to realise this potential.

Development of consensus-driven SPIRIT and CONSORT extensions for early phase dose-finding trials: the DEFINE study

BACKGROUND

Early phase dose-finding (EPDF) trials are crucial for the development of a new intervention and influence whether it should be investigated in further trials. Guidance exists for clinical trial protocols and completed trial reports in the SPIRIT and CONSORT guidelines, respectively. However, both guidelines and their extensions do not adequately address the characteristics of EPDF trials. Building on the SPIRIT and CONSORT checklists, the DEFINE study aims to develop international consensus-driven guidelines for EPDF trial protocols (SPIRIT-DEFINE) and reports (CONSORT-DEFINE).

METHODS

The initial generation of candidate items was informed by reviewing published EPDF trial reports. The early draft items were refined further through a review of the published and grey literature, analysis of real-world examples, citation and reference searches, and expert recommendations, followed by a two-round modified Delphi process. Patient and public involvement and engagement (PPIE) was pursued concurrently with the quantitative and thematic analysis of Delphi participants' feedback.

RESULTS

The Delphi survey included 79 new or modified SPIRIT-DEFINE (n = 36) and CONSORT-DEFINE (n = 43) extension candidate items. In Round One, 206 interdisciplinary stakeholders from 24 countries voted and 151 stakeholders voted in Round Two. Following Round One feedback, one item for CONSORT-DEFINE was added in Round Two. Of the 80 items, 60 met the threshold for inclusion (≥ 70% of respondents voted critical: 26 SPIRIT-DEFINE, 34 CONSORT-DEFINE), with the remaining 20 items to be further discussed at the consensus meeting. The parallel PPIE work resulted in the development of an EPDF lay summary toolkit consisting of a template with guidance notes and an exemplar.

CONCLUSIONS

By detailing the development journey of the DEFINE study and the decisions undertaken, we envision that this will enhance understanding and help researchers in the development of future guidelines. The SPIRIT-DEFINE and CONSORT-DEFINE guidelines will allow investigators to effectively address essential items that should be present in EPDF trial protocols and reports, thereby promoting transparency, comprehensiveness, and reproducibility.

TRIAL REGISTRATION

SPIRIT-DEFINE and CONSORT-DEFINE are registered with the EQUATOR Network ( https://www.equator-network.org/ ).

A novel model of the continual reassessment method in Phase I trial

For the model-based designs, the continual reassessment method (CRM) is widely used to identify the maximum tolerated dose (MTD) in phase I clinical trials. To improve the performance of classic CRM models, we propose a new CRM and its dose-toxicity probability function based on the Cox model whatever the treatment response is immediately observed or delayed. In the process of dose-finding trial, we can use our model in situations when either the response is delayed or not and can derive the likelihood function and posterior mean toxicity probabilities to find the MTD. Simulation is carried out to evaluate the performance of the proposed model with the classic CRM models. We also evaluate the operating characteristics of the proposed model by the Efficiency, Accuracy, Reliability, and Safety (EARS) criteria.

Performance of phase-I dose finding designs with and without a run-in intra-patient dose escalation stage

Dose-finding designs for phase-I trials aim to determine the recommended phase-II dose (RP2D) for further phase-II drug development. If the trial includes patients for whom several lines of standard therapy failed or if the toxicity of the investigated agent does not necessarily increase with dose, optimal dose-finding designs should limit the frequency of treatment with suboptimal doses. We propose a two-stage design strategy with a run-in intra-patient dose escalation part followed by a more traditional dose-finding design. We conduct simulation studies to compare the 3 + 3 design, the Bayesian Optimal Interval Design (BOIN) and the Continual Reassessment Method (CRM) with and without intra-patient dose escalation. The endpoints are accuracy, sample size, safety, and therapeutic efficiency. For scenarios where the correct RP2D is the highest dose, inclusion of an intra-patient dose escalation stage generally increases accuracy and therapeutic efficiency. However, for scenarios where the correct RP2D is below the highest dose, intra-patient dose escalation designs lead to increased risk of overdosing and an overestimation of RP2D. The magnitude of the change in operating characteristics after including an intra-patient stage is largest for the 3 + 3 design, decreases for the BOIN and is smallest for the CRM.

Adaptive clinical trials and master protocols

Methodologies for randomized, double-blind, placebo-controlled clinical trials continue to develop in concert with evolving scientific and translational knowledge. Adaptive trial designs, in which data generated during the study are used to modify subsequent study activity (i.e., sample sizes, entry criteria, or outcomes), can optimize flexibility and expedite the safety and efficacy assessments for interventions of interest. This chapter will summarize general designs, advantages, and pitfalls associated with adaptive clinical trials and compare their features with those of conventional trial designs. It will also review novel ways for which seamless designs and master protocols may improve trial efficiency while offering interpretable data.

Project Optimus, an FDA initiative: Considerations for cancer drug development internationally, from an academic perspective

Modern cancer therapeutics are increasingly targeted, bringing the promise of new and improved activity, alongside better tolerability. However, while many are indeed resulting in dramatic improvements in disease control and patient survival, short- and long-term tolerability has not always accompanied it. The choice of dose and schedule is often in the upper range of the therapeutic window, driven by the maximum tolerated dose (MTD) model of previous cytotoxic agents. There is increasing recognition that this needs to change, by taking a more holistic approach to determine the optimal dose for desired biological effects and tolerability early in clinical development. In the US, the FDA's Oncology Centre of Excellence is addressing this via the Project Optimus initiative: aiming to reform dose optimisation studies so that they can demonstrate the most appropriate dose selection. Early clinical development will need to demonstrate the dose-exposure, -pharmacodynamic, -toxicity and -activity relationships, including randomised evaluations for dose selection. Regulatory agencies outside the US are similarly exploring this. Along with Australia, Brazil, Canada, Israel, Singapore and Switzerland, the UK participates in Project Orbis, a collaborative program with the FDA to accelerate patient access to new cancer medicines through coordinated regulatory review. Close alignment with Project Optimus will be important internationally and will require changes across industry, including for academic units and small biotech. We discuss our perspective on the implications, and opportunities, for early phase oncology trials as a uniquely charity-funded drug development facility, the Centre for Drug Development within the Cancer Research UK charity.

Practical implementation of the partial ordering continual reassessment method in a Phase I combination-schedule dose-finding trial

There is a growing medical interest in combining several agents and optimizing their dosing schedules in a single trial in order to optimize the treatment for patients. Evaluating at doses of several drugs and their scheduling in a single Phase I trial simultaneously possess a number of statistical challenges, and specialized methods to tackle these have been proposed in the literature. However, the uptake of these methods is slow and implementation examples of such advanced methods are still sparse to date. In this work, we share our experience of proposing a model-based partial ordering continual reassessment method (POCRM) design for three-dimensional dose-finding in an oncology trial. In the trial, doses of two agents and the dosing schedule of one of them can be escalated/de-escalated. We provide a step-by-step summary on how the POCRM design was implemented and communicated to the trial team. We proposed an approach to specify toxicity orderings and their a-priori probabilities, and developed a number of visualization tools to communicate the statistical properties of the design. The design evaluation included both a comprehensive simulation study and considerations of the individual trial behavior. The study is now enrolling patients. We hope that sharing our experience of the successful implementation of an advanced design in practice that went through evaluations of several health authorities will facilitate a better uptake of more efficient methods in practice.

Surv-CRM-12: A Bayesian phase I/II survival CRM for right-censored toxicity endpoints with competing disease progression

The growing interest in new classes of anti-cancer agents, such as molecularly-targeted therapies and immunotherapies with modes of action different from those of cytotoxic chemotherapies, has changed the dose-finding paradigm. In this setting, the observation of late-onset toxicity endpoints may be precluded by treatment and trial discontinuation due to disease progression, defining a competing event to toxicity. Trial designs where dose-finding is modeled in the framework of a survival competing risks model appear particularly well-suited. We aim to provide a phase I/II dose-finding design that allows dose-limiting toxicity (DLT) outcomes to be delayed or unobserved due to competing progression within the possibly long observation window. The proposed design named the Survival-continual reassessment method-12, uses survival models for right-censored DLT and progression endpoints. In this competing risks framework, cause-specific hazards for DLT and progression-free of DLT were considered, with model parameters estimated using Bayesian inference. It aims to identify the optimal dose (OD), by minimizing the cumulative incidence of disease progression, given an acceptable toxicity threshold. In a simulation study, design operating characteristics were evaluated and compared to the TITE-BOIN-ET design and a nonparametric benchmark approach. The performance of the proposed method was consistent with the complexity of scenarios as assessed by the nonparametric benchmark. We found that the proposed design presents satisfying operating characteristics in selecting the OD and safety.

A Road Map for Designing Phase I Clinical Trials of Radiotherapy-Novel Agent Combinations

Radiotherapy has proven efficacy in a wide range of cancers. There is growing interest in evaluating radiotherapy-novel agent combinations and a drive to initiate this earlier in the clinical development of the novel agent, where the scientific rationale and preclinical evidence for a radiotherapy combination approach are high. Optimal design, delivery, and interpretation of studies are essential. In particular, the design of phase I studies to determine safety and dosing is critical to an efficient development strategy. There is significant interest in early-phase research among scientific and clinical communities over recent years, at a time when the scrutiny of the trial methodology has significantly increased. To enhance trial design, optimize safety, and promote efficient trial conduct, this position paper reviews the current phase I trial design landscape. Key design characteristics extracted from 37 methodology papers were used to define a road map and a design selection process for phase I radiotherapy-novel agent trials. Design selection is based on single- or dual-therapy dose escalation, dose-limiting toxicity categorization, maximum tolerated dose determination, subgroup evaluation, software availability, and design performance. Fifteen of the 37 designs were identified as being immediately accessible and relevant to radiotherapy-novel agent phase I trials. Applied examples of using the road map are presented. Developing these studies is intensive, highlighting the need for funding and statistical input early in the trial development to ensure appropriate design and implementation from the outset. The application of this road map will improve the design of phase I radiotherapy-novel agent combination trials, enabling a more efficient development pathway.

From innovative thinking to pharmaceutical industry implementation: Some success stories

In industry, successful innovation involves not only developing new statistical methodology, but also ensuring that this methodology is implemented successfully. This includes enabling applied statisticians to understand the method, its benefits and limitations and empowering them to implement the new method. This will include advocacy, influencing in-house and external stakeholders, such that these stakeholders are receptive to the new methodology. In this paper, we describe some industry successes and focus on our colleague, Andy Grieve's role in these.

Mathematical Modelling for Optimal Vaccine Dose Finding: Maximising Efficacy and Minimising Toxicity

Vaccination is a key tool to reduce global disease burden. Vaccine dose can affect vaccine efficacy and toxicity. Given the expense of developing vaccines, optimising vaccine dose is essential. Mathematical modelling has been suggested as an approach for optimising vaccine dose by quantitatively establishing the relationships between dose and efficacy/toxicity. In this work, we performed simulation studies to assess the performance of modelling approaches in determining optimal dose. We found that the ability of modelling approaches to determine optimal dose improved with trial size, particularly for studies with at least 30 trial participants, and that, generally, using a peaking or a weighted model-averaging-based dose–efficacy relationship was most effective in finding optimal dose. Most methods of trial dose selection were similarly effective for the purpose of determining optimal dose; however, including modelling to adapt doses during a trial may lead to more trial participants receiving a more optimal dose. Clinical trial dosing around the predicted optimal dose, rather than only at the predicted optimal dose, may improve final dose selection. This work suggests modelling can be used effectively for vaccine dose finding, prompting potential practical applications of these methods in accelerating effective vaccine development and saving lives.

Selumetinib in combination with dexamethasone for the treatment of relapsed/refractory RAS-pathway mutated paediatric and adult acute lymphoblastic leukaemia (SeluDex): study protocol for an international, parallel-group, dose-finding with expansion phase I/II trial

INTRODUCTION

Event-free survival rates at 15 years for paediatric patients with relapsed/refractory acute lymphoblastic leukaemia (ALL) are 30%-50%, with 5-year survival for adult patients only 20%. Many patients with newly diagnosed and relapsed ALL harbour somatic RAS-signalling activation mutations. Induction therapy for ALL involves steroids, with preclinical data suggesting the combination of dexamethasone with the MEK1/2 inhibitor, selumetinib (ARRY-142886) has a synergistic anticancer effect.

METHODS AND ANALYSIS

The SeluDex trial is an international, parallel-group, dose-finding with expansion, phase I/II trial to assess the selumetinib/dexamethasone combination in adult and paediatric patients with relapsed/refractory, RAS pathway mutant ALL. The Cancer Research UK Clinical Trials Unit at University of Birmingham is the UK Coordinating Centre, with national hubs in Copenhagen, Denmark; Monza, Italy; Münster, Germany; Paris, France; and Utrecht, Netherlands. Patients with morphologically proven relapsed/refractory or progressive B-cell precursor or T-cell ALL, with demonstrated RAS pathway activating mutations are eligible. Adult patients are >18 years old, ECOG <2 and paediatric <18 years old, Lansky play scale ≥60% or Karnofsky score ≥60%. Phase I primary objective is the recommended phase II dose of selumetinib as defined by occurrence/non-occurrence of dose limiting toxicities using the continual reassessment method; phase II will evaluate preliminary antileukaemic activity of the combination, as defined by morphological response 28 days post-treatment using a Bayesian approach. Target recruitment is between 26 and 42 patients (minimum 13 and maximum 21 per group), depending the number of phase I patients included in phase II.

ETHICS AND DISSEMINATION

Medical ethical committees of all the participating countries have approved the study protocol; initial (UK) ethics approval (17/YH/0123) was granted by Yorkshire & The Humber-Leeds West Research Ethics Committee. Participants are required to provide written informed consent/assent. Results will be disseminated through national and international presentations and peer-reviewed publications.

TRIAL REGISTRATION NUMBER

ISRCTN92323261.

Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic

BACKGROUND

Modern designs for dose-finding studies (e.g., model-based designs such as continual reassessment method) have been shown to substantially improve the ability to determine a suitable dose for efficacy testing when compared to traditional designs such as the 3 + 3 design. However, implementing such designs requires time and specialist knowledge.

METHODS

We present a practical approach to developing a model-based design to help support uptake of these methods; in particular, we lay out how to derive the necessary parameters and who should input, and when, to these decisions. Designing a model-based, dose-finding trial is demonstrated using a treatment within the AGILE platform trial, a phase I/II adaptive design for novel COVID-19 treatments.

RESULTS

We present discussion of the practical delivery of AGILE, covering what information was found to support principled decision making by the Safety Review Committee, and what could be contained within a statistical analysis plan. We also discuss additional challenges we encountered in the study and discuss more generally what (unplanned) adaptations may be acceptable (or not) in studies using model-based designs.

CONCLUSIONS

This example demonstrates both how to design and deliver an adaptive dose-finding trial in order to support uptake of these methods.

First-in-human dose escalation study of cyclin-dependent kinase-9 inhibitor VIP152 in patients with advanced malignancies shows early signs of clinical efficacy

PURPOSE

To report on the first-in-human phase I study of VIP152 (NCT02635672), a potent and highly selective CDK9 inhibitor.

PATIENTS AND METHODS

Adults with solid tumors or aggressive non-Hodgkin lymphoma (NHL) who were refractory to or had exhausted all available therapies received VIP152 monotherapy as a 30-minute intravenous, once weekly infusion, as escalating doses (5, 10, 15, 22.5, or 30 mg in 21-day cycles) until the maximum tolerated dose (MTD) was determined.

RESULTS

Thirty-seven patients received {greater than or equal to} 1 VIP152 dose, with 30 mg identified as the MTD based on dose-limiting toxicity of grade 3/4 neutropenia. The most common adverse events were nausea and vomiting (75.7% and 56.8%, respectively), all of grade 1/2 severity. Of the most common events, Grade 3/4 events occurring in > 1 patient were neutropenia (22%), anemia (11%), abdominal pain (8%), increased alkaline phosphatase (8%), and hyponatremia (8%). Day 1 exposure for the MTD exceeded the predicted minimum therapeutic exposure and reproducibly achieved maximal pathway modulation; no accumulation occurred after multiple doses. Seven of 30 patients with solid tumors had stable disease (including 9.5 and 16.8 months in individual patients with pancreatic cancer and salivary gland cancer, respectively), and 2 of 7 patients with high-grade B-cell lymphoma with MYC and BCL2/BCL6 translocations (HGL) achieved durable complete metabolic remission (ongoing at study discontinuation, after 3.7 and 2.3 years of treatment).

CONCLUSION

VIP152 monotherapy, administered intravenously once weekly, demonstrated a favorable safety profile and evidence of clinical benefit in patients with advanced HGL and solid tumors.

Protocol: Pentoxifylline optimal dose finding trial in preterm neonates with suspected late onset sepsis (PTX-trial)

BACKGROUND

Late onset sepsis is a leading cause of death and morbidity in preterm infants. Despite optimal antibiotic treatment, sepsis related mortality and morbidity is still high. Pentoxifylline (PTX) is a methylxanthine with promising immunomodulatory properties, which can be used as an additional therapy next to antibiotics in preterm infants. PTX is increasingly used off-label in neonatal intensive care units, however up till now no dose finding study has been done for PTX in this specific population. The aim of this study (PTX-trial) is to determine the optimal dose of PTX in preterm infants (gestational age < 30 weeks) with (suspected) late onset sepsis. Dose finding in this particular population is unique, since for most drugs used in neonates the optimal dosage has not been investigated in phase II dose-seeking studies.

METHODS

The PTX-trial is a prospective open label sequential dose-optimization study with an adapted continual reassessment method. An up-and-down dose-response design will be used, with dose step-up and step-down titration after every 3 patients. The PTX starting dosage will be 30 mg/kg/day in 6 hours as described in most previous neonatal studies. Efficacy is defined by means of biochemical and clinical parameters. Toxicity in these vulnerable patients is unwarranted. The optimal dose is defined as the ED75 (i.e., clinically and chemically effective dose for 75% of patients) in preterm neonates with late onset sepsis. We plan to include 30 neonates to determine the optimal dose using this study design. Subsequently, the optimal dose will be validated in 10 additional preterm neonates. In parallel, pharmacokinetics of PTX and its metabolites will be described as well as longitudinal evaluation of metabolomics and proteomics.

DISCUSSION

The study has been approved by the Regional Medical Ethics Board of Erasmus Medical Center University Rotterdam (MEC 2019-0477) and registered at Clinicaltrials.gov (NCT04152980). Results of the main trial and each of the secondary endpoints will be submitted for publications in peer-reviewed journals.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT04152980 , Registered November 6th, 2019.

Healthy volunteers in first-in-human oncology drug development for small molecules

This review provides tools to consider the inclusion of healthy volunteers (HVs) in first-in-human (FIH) oncology clinical trials with small molecules, including targeted and immunomodulatory agents, a strategy that was not envisioned with classic chemotherapy. To enable an FIH oncology trial in HVs compared to cancer patients (CPs), a robust nonclinical package must be generated, which includes toxicokinetic and pharmacokinetic studies, as well as more extensive safety pharmacology, toxicology and genotoxicity studies. This strategy could provide an early clinical characterization of the pharmacokinetic parameters and clinical safety profile in the absence of comorbidities and concomitant medication. It also avoids the ethical issue of administrating subtherapeutic doses to CPs, and could potentially help to accelerate the timelines of clinical drug development for patient care. That being said, stakeholders involved in these studies need to proceed with caution, fully understand the regulatory guidance and thoroughly evaluate the benefits and risks. This paper serves to address the regulatory guidance and other considerations needed when using healthy volunteers in early oncology trials.

Early-Phase Interventional Trials in Oral Cancer Prevention

Simple Summary

Oral cancer is a devastating disease with increasing incidence worldwide. Oral epithelial dysplasia (OED) is a potentially malignant disorder and patients with OED are at increased risk of developing oral cancer. Current strategies for management of OED include surgery or close observation and both fail to address the underlying pathogenesis of the disease. There is an urgent need for evidence-based medical treatments for OED to prevent oral cancer development in this cohort. Chemoprevention trials to date have not delivered therapeutic agents for routine clinical practice. Historically, there has been significant heterogeneity in the design of oral cancer chemoprevention trials, with most failing to selectively recruit patients with biopsy-proven OED, which limits the usefulness of the findings in the OED population. The present paper aims to review the current evidence and the methodology of early-phase trials in oral cancer chemoprevention. Novel strategies in oral cancer chemoprevention will also be discussed.

Abstract

The increasing breadth of molecular targets, promise of immune-targeted therapies and repurposed agents have heightened interest in cancer prevention. While, to date, testing of oral cancer chemoprevention strategies has failed to deliver therapeutic agents for routine clinical practice, there remains an urgent need for further clinical research to overcome this hurdle. Patients at the greatest risk of disease stand to benefit the most from inclusion in clinical trials; therefore, there is a need to carefully define this population using validated clinical and molecular markers. Safety, tolerability and the efficacy of interventions is assessed through carefully selected endpoints. These endpoints may include pharmacodynamic, clinical, histological and on-target molecular modifications as an individual or as a composite endpoint. Early-phase trials provide an area of opportunity to explore novel and repurposed agents in the setting of oral cancer chemoprevention, eventually leading to phase III trials with clinical endpoints such as transformation and clinical outcome; these studies are large, lengthy and expensive and should be reserved for the most promising of agents. This paper will explore current evidence in oral cancer chemoprevention, drug repurposing, selection of appropriate endpoints for early-phase trials and novel therapeutic angles in oral cancer chemoprevention.

TRAFIC: statistical design and analysis plan for a pragmatic early phase 1/2 Bayesian adaptive dose escalation trial in rheumatoid arthritis

Background

Adaptive model-based dose-finding designs have demonstrated advantages over traditional rule-based designs but have increased statistical complexity but uptake has been slow especially outside of cancer trials. TRAFIC is a multi-centre, early phase trial in rheumatoid arthritis incorporating a model-based design.

Methods

A Bayesian adaptive dose-finding phase I trial rolling into a single-arm, single-stage phase II trial. Model parameters for phase I were chosen via Monte Carlo simulation evaluating objective performance measures under clinically relevant scenarios and incorporated stopping rules for early termination. Potential designs were further calibrated utilising dose transition pathways.

Discussion

TRAFIC is an MRC-funded trial of a re-purposed treatment demonstrating that it is possible to design, fund and implement a model-based phase I trial in a non-cancer population within conventional research funding tracks and regulatory constraints. The phase I design allows borrowing of information from previous trials, all accumulated data to be utilised in decision-making, verification of operating characteristics through simulation, improved understanding for management and oversight teams through dose transition pathways. The rolling phase II design brings efficiencies in trial conduct including site and monitoring activities and cost.

TRAFIC is the first funded model-based dose-finding trial in inflammatory disease demonstrating that small phase I/II trials can have an underlying statistical basis for decision-making and interpretation.

Trial registration

Trials Registration: ISRCTN, ISRCTN36667085. Registered on September 26, 2014.

Is more better? An analysis of toxicity and response outcomes from dose-finding clinical trials in cancer

BACKGROUND

The overwhelming majority of dose-escalation clinical trials use methods that seek a maximum tolerable dose, including rule-based methods like the 3+3, and model-based methods like CRM and EWOC. These methods assume that the incidences of efficacy and toxicity always increase as dose is increased. This assumption is widely accepted with cytotoxic therapies. In recent decades, however, the search for novel cancer treatments has broadened, increasingly focusing on inhibitors and antibodies. The rationale that higher doses are always associated with superior efficacy is less clear for these types of therapies.

METHODS

We extracted dose-level efficacy and toxicity outcomes from 115 manuscripts reporting dose-finding clinical trials in cancer between 2008 and 2014. We analysed the outcomes from each manuscript using flexible non-linear regression models to investigate the evidence supporting the monotonic efficacy and toxicity assumptions.

RESULTS

We found that the monotonic toxicity assumption was well-supported across most treatment classes and disease areas. In contrast, we found very little evidence supporting the monotonic efficacy assumption.

CONCLUSIONS

Our conclusion is that dose-escalation trials routinely use methods whose assumptions are violated by the outcomes observed. As a consequence, dose-finding trials risk recommending unjustifiably high doses that may be harmful to patients. We recommend that trialists consider experimental designs that allow toxicity and efficacy outcomes to jointly determine the doses given to patients and recommended for further study.

Would the Recommended Dose Have Been Different Using Novel Dose-Finding Designs? Comparing Dose-Finding Designs in Published Trials

Simulation studies have shown that novel designs such as the continual reassessment method and the Bayesian optimal interval (BOIN) design outperform the 3 + 3 design by recommending the maximum tolerated dose (MTD) more often, using less patients, and allotting more patients to the MTD. However, it is not clear whether these novel designs would have yielded different results in the context of real-world dose-finding trials. This is a commonly mentioned reason for the continuous use of 3 + 3 designs for oncology trials, with investigators considering simulation studies not sufficiently convincing to warrant the additional design complexity of novel designs.

METHODS

We randomly sampled 60 published dose-finding trials to obtain 22 that used the 3 + 3 design, identified an MTD, published toxicity data, and had more than two dose levels. We compared the published MTD with the estimated MTD using the continual reassessment method and BOIN using target toxicity rates of 25% and 30% and toxicity data from the trial. Moreover, we compared patient allocation and sample size assuming that these novel designs had been implemented.

RESULTS

Model-based designs chose dose levels higher than the published MTD in about 40% of the trials, with estimated and observed toxicity rates closer to the target toxicity rates of 25% and 30%. They also assigned less patients to suboptimal doses and permitted faster dose escalation.

CONCLUSION

This study using published dose-finding trials shows that novel designs would recommend different MTDs and confirms the advantages of these designs compared with the 3 + 3 design, which were demonstrated by simulation studies.

Multidimensional Phase I Dose Ranging Trials for Stroke Recovery Interventions: Key Challenges and How to Address Them

Despite an increase in the amount of published stroke recovery research, interventions have failed to markedly affect the trajectory of recovery poststroke. We argue that early-phase research to systematically investigate dose is an important contributor to advance the science underpinning stroke recovery. In this article, we aim to (a) define the problem of insufficient use of a systematic approach to early-phase, multidimensional dose articulation research and (b) propose a solution that applies this approach to design a multidimensional phase I trial to identify the maximum tolerated dose (MTD). We put forward a design template as a decision support tool to increase knowledge of how to develop a phase I dose-ranging trial for nonpharmaceutical stroke recovery interventions. This solution has the potential to advance the development of efficacious stroke recovery interventions, which include activity-based rehabilitation interventions.

Moving Beyond 3+3: The Future of Clinical Trial Design

Misgivings have been raised about the operating characteristics of the canonical 3+3 dose-escalation phase I clinical trial design. Yet, the traditional 3+3 design is still the most commonly used. Although it has been implied that adhering to this design is due to a stubborn reluctance to adopt change despite other designs performing better in hypothetical computer-generated simulation models, the continued adherence to 3+3 dose-escalation phase I strategies is more likely because these designs perform the best in the real world, pinpointing the correct dose and important side effects with an acceptable degree of precision. Beyond statistical simulations, there are little data to refute the supposed shortcomings ascribed to the 3+3 method. Even so, to address the unique nuances of gene- and immune-targeted compounds, a variety of inventive phase 1 trial designs have been suggested. Strategies for developing these therapies have launched first-in-human studies devised to acquire a breadth of patient data that far exceed the size of a typical phase I design and blur the distinction between dose selection and efficacy evaluation. Recent phase I trials of promising cancer therapies assessed objective tumor response and durability at various doses and schedules as well as incorporated multiple expansion cohorts spanning a variety of histology or biomarker-defined tumor subtypes, sometimes resulting in U.S. Food and Drug Administration approval after phase I. This article reviews recent innovations in phase I design from the perspective of multiple stakeholders and provides recommendations for future trials.

Targeting synovial fibroblast proliferation in rheumatoid arthritis (TRAFIC): an open-label, dose-finding, phase 1b trial

Background

Current rheumatoid arthritis therapies target immune inflammation and are subject to ceiling effects. Seliciclib is an orally available cyclin-dependent kinase inhibitor that suppresses proliferation of synovial fibroblasts—cells not yet targeted in rheumatoid arthritis. Part 1 of this phase 1b/2a trial aimed to establish the maximum tolerated dose of seliciclib in patients with active rheumatoid arthritis despite ongoing treatment with TNF inhibitors, and to evaluate safety and pharmacokinetics.

Methods

Phase 1b of the TRAFIC study was a non-randomised, open-label, dose-finding trial done in rheumatology departments in five UK National Health Service hospitals. Eligible patients (aged ≥18 years) fulfilled the 1987 American College of Rheumatology (ACR) or the 2010 ACR–European League Against Rheumatism classification criteria for rheumatoid arthritis and had moderate to severe disease activity (a Disease Activity Score for 28 joints [DAS28] of ≥3·2) despite stable treatment with anti-TNF therapy for at least 3 months before enrolment. Participants were recruited sequentially to a maximum of seven cohorts of three participants each, designated to receive seliciclib 200 mg, 400 mg, 600 mg, 800 mg, or 1000 mg administered in 200 mg oral capsules. Sequential cohorts received doses determined by a restricted, one-stage Bayesian continual reassessment model, which determined the maximum tolerated dose (the primary outcome) based on a target dose-limiting toxicity rate of 35%. Seliciclib maximum concentration (C_max) and area under the plasma concentration time curve 0–6 h (AUC_0–6) were measured. This study is registered with ISRCTN, ISRCTN36667085.

Findings

Between Oct 8, 2015, and Aug 15, 2017, 37 patients were screened and 15 were enrolled to five cohorts and received seliciclib, after which the trial steering committee and the data monitoring committee determined that the maximum tolerated dose could be defined. In addition to a TNF inhibitor, ten (67%) enrolled patients were taking conventional synthetic disease modifying antirheumatic drugs. The maximum tolerated dose of seliciclib was 400 mg, with an estimated dose-limiting toxicity probability of 0·35 (90% posterior probability interval 0·18–0·52). Two serious adverse events occurred (one acute kidney injury in a patient receiving the 600 mg dose and one drug-induced liver injury in a patient receiving the 400 mg dose), both considered to be related to seliciclib and consistent with its known safety profile. 65 non-serious adverse events occurred during the trial, 50 of which were considered to be treatment related. Most treatment-related adverse events were mild; 20 of the treatment-related non-serious adverse events contributed to dose-limiting toxicities. There were no deaths. Average C_max and AUC_0–6 were two-times higher in participants developing dose-limiting toxicities.

Interpretation

The maximum tolerated dose of seliciclib has been defined for rheumatoid arthritis refractory to TNF blockade. No unexpected safety concerns were identified to preclude ongoing clinical evaluation in a formal efficacy trial.

Funding

UK Medical Research Council, Cyclacel, Research into Inflammatory Arthritis Centre (Versus Arthritis), and the National Institute of Health Research Newcastle and Birmingham Biomedical Research Centres and Clinical Research Facilities.

Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.

A simulation-free approach to assessing the performance of the continual reassessment method

The continual reassessment method (CRM) is an adaptive design for Phase I trials whose operating characteristics, including appropriate sample size, probability of correctly identifying the maximum tolerated dose, and the expected proportion of participants assigned to each dose, can only be determined via simulation. The actual time to determine a final "best" design can take several hours or days, depending on the number of scenarios that are examined. The computational cost increases as the kernel of the one-parameter CRM design is expanded to other settings, including additional parameters, monitoring of both toxicity and efficacy, and studies of combinations of two agents. For a given vector of true DLT probabilities, we have developed an approach that replaces a simulation study of thousands of hypothetical trials with a single simulation. Our approach, which is founded on the consistency of the CRM, very accurately reflects the results produced by the simulation study, but does so in a fraction of time required by the simulation study. Relative to traditional simulations, we extensively examine how our method is able to assess the operating characteristics of a CRM design for a hypothetical trial whose characteristics are based upon a previously published Phase I trial. We also provide a metric of nonconsistency and demonstrate that although nonconsistency can impact the operating characteristics of our method, the degree of over- or under-estimation is unpredictable. As a solution, we provide an algorithm for maintaining the consistency of a chosen CRM design so that our method is applicable for any trial.

Innovative approaches in CNS clinical drug development: Quantitative systems pharmacology

Clinical trials involving brain disorders are notoriously difficult to set up and run. Innovative ways to develop effective prevention and treatment strategies for central nervous system (CNS) diseases are urgently needed. New approaches that are likely to renew or at least modify the paradigms used so far have been recently proposed. Quantitative systems pharmacology (QSP) uses mathematical computerized models to characterize biological systems, disease processes and CNS drug pharmacology. Integrated experimental medicine should increase the probability and predictability of success while controlling clinical trials costs. Finally, the societal perspective and patient empowerment also offer additional approaches to demonstrate the benefit of a new drug in the CNS field.

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies.

A Bayesian phase 2 model based adaptive design to optimise antivenom dosing: Application to a dose-finding trial for a novel Russell's viper antivenom in Myanmar

For most antivenoms there is little information from clinical studies to infer the relationship between dose and efficacy or dose and toxicity. Antivenom dose-finding studies usually recruit too few patients (e.g. fewer than 20) relative to clinically significant event rates (e.g. 5%). Model based adaptive dose-finding studies make efficient use of accrued patient data by using information across dosing levels, and converge rapidly to the contextually defined 'optimal dose'. Adequate sample sizes for adaptive dose-finding trials can be determined by simulation. We propose a model based, Bayesian phase 2 type, adaptive clinical trial design for the characterisation of optimal initial antivenom doses in contexts where both efficacy and toxicity are measured as binary endpoints. This design is illustrated in the context of dose-finding for Daboia siamensis (Eastern Russell's viper) envenoming in Myanmar. The design formalises the optimal initial dose of antivenom as the dose closest to that giving a pre-specified desired efficacy, but resulting in less than a pre-specified maximum toxicity. For Daboia siamensis envenoming, efficacy is defined as the restoration of blood coagulability within six hours, and toxicity is defined as anaphylaxis. Comprehensive simulation studies compared the expected behaviour of the model based design to a simpler rule based design (a modified '3+3' design). The model based design can identify an optimal dose after fewer patients relative to the rule based design. Open source code for the simulations is made available in order to determine adequate sample sizes for future adaptive snakebite trials. Antivenom dose-finding trials would benefit from using standard model based adaptive designs. Dose-finding trials where rare events (e.g. 5% occurrence) are of clinical importance necessitate larger sample sizes than current practice. We will apply the model based design to determine a safe and efficacious dose for a novel lyophilised antivenom to treat Daboia siamensis envenoming in Myanmar.

Current status of Bayesian clinical trials for oncology, 2020

Bayesian methods had established a foothold in developing therapies in oncology trials. Methods: We identified clinical trials posted on the ClinicalTrials.gov database focused on Oncology trials with a Bayesian approach in their design. Differences in study characteristics such as design, study phase, randomization, masking, purpose of study, main outcomes, gender, age and funding involvement according to Bayesian approach were assessed using Chi-squared or Fisher's exact tests. Results: We identified 225 studies with Bayesian components in their design addressing oncological diseases. The most common designs were Bayesian Toxicity Monitoring (26.4%), Model-based designs (36%) Model-assisted designs (8%). Statistical methods such as Bayesian logistic regression model (59.4%), Bayesian piecewise exponential survival regression (10.9%) and the Continual reassessment method (9.4%) were the most used. Conclusions: Bayesian trials are more common in the early phases of drug development specifically in phase II trials (43.6%). Cancer institutes or Hospitals funded most of the studies retrieved. This type of design has increased over time and represent an innovative means of increasing trial efficiency.

Transmuscular quadratus lumborum block for percutaneous nephrolithotomy: Study protocol for a dose-finding trial

BACKGROUND

The objective of this trial is to optimize the transmuscular quadratus lumborum (TQL) block, by investigating the minimal effective volume (MEV₉₀ ) of ropivacaine 0.75% for single-shot TQL block in percutaneous nephrolithotomy (PNL) patients.

METHODS

This double-blind, randomized and controlled dose-finding trial is based on a biased coin up-and-down sequential design, where the volume of local anaesthetic administered to each patient depends on the response from the previous one. Investigating the TQL block, the first patient recruited receives 20 ml ropivacaine 0.75% preoperatively. In case of block failure, the next patient will receive the same volume with an increment of 2 ml. Given a successful block for the first patient, the next patient will be randomized to either a lower volume (previous volume with a reduction of 2 ml), or the same volume as the previous patient. The respective probabilities being b = 0.11 for a reduced volume and 1-b = 0.89 for the same volume. Block success is defined as patient reported pain score numeric rated scale (NRS) ≤3 (0-10/10) 30 minutes after arrival in the post anaesthesia care unit (PACU). The NRS pain score is our primary and only outcome for block success. A minimum of 25 eligible patients are needed to achieve precise estimation of MEV₉₀ with narrow 95% confidence intervals derived by bootstrapping.

DISCUSSION

Recruiting will begin June 2020 and is expected to finish November 2020. Data analysis will be performed at interims during and after the study. Results will be published in an international peer-reviewed medical journal.

Practicalities in running early-phase trials using the time-to-event continual reassessment method (TiTE-CRM) for interventions with long toxicity periods using two radiotherapy oncology trials as examples

BACKGROUND

Awareness of model-based designs for dose-finding studies such as the Continual Reassessment Method (CRM) is now becoming more commonplace amongst clinicians, statisticians and trial management staff. In some settings toxicities can occur a long time after treatment has finished, resulting in extremely long, interrupted, CRM design trials. The Time-to-Event CRM (TiTE-CRM), a modification to the original CRM, accounts for the timing of late-onset toxicities and results in shorter trial duration. In this article, we discuss how to design and deliver a trial using this method, from the grant application stage through to dissemination, using two radiotherapy trials as examples.

METHODS

The TiTE-CRM encapsulates the dose-toxicity relationship with a statistical model. The model incorporates observed toxicities and uses a weight to account for the proportion of completed follow-up of participants without toxicity. This model uses all available data to determine the next participant's dose and subsequently declare the maximum tolerated dose. We focus on two trials designed by the authors to illustrate practical issues when designing, setting up, and running such studies.

RESULTS

In setting up a TiTE-CRM trial, model parameters need to be defined and the time element involved might cause complications, therefore looking at operating characteristics through simulations is essential. At the grant application stage, we suggest resources to fund statisticians' time before funding is awarded and make recommendations for the level of detail to include in funding applications. While running the trial, close contact of all involved staff is required as a dose decision is made each time a participant is recruited. We suggest ways of capturing data in a timely manner and give example code in R for design and delivery of the trial. Finally, we touch upon dissemination issues while the trial is running and upon completion.

CONCLUSION

Model-based designs can be complex. We hope this paper will help clinical trial teams to demystify the conduct of TiTE-CRM trials and be a starting point for using this methodology in practice.

Designing and evaluating dose-escalation studies made easy: The MoDEsT web app

BACKGROUND/AIMS

Dose-escalation studies are essential in the early stages of developing novel treatments, when the aim is to find a safe dose for administration in humans. Despite their great importance, many dose-escalation studies use study designs based on heuristic algorithms with well-documented drawbacks. Bayesian decision procedures provide a design alternative that is conceptually simple and methodologically sound, but very rarely used in practice, at least in part due to their perceived statistical complexity. There are currently very few easily accessible software implementations that would facilitate their application.

METHODS

We have created MoDEsT, a free and easy-to-use web application for designing and conducting single-agent dose-escalation studies with a binary toxicity endpoint, where the objective is to estimate the maximum tolerated dose. MoDEsT uses a well-established Bayesian decision procedure based on logistic regression. The software has a user-friendly point-and-click interface, makes changes visible in real time, and automatically generates a range of graphs, tables, and reports. It is aimed at clinicians as well as statisticians with limited expertise in model-based dose-escalation designs, and does not require any statistical programming skills to evaluate the operating characteristics of, or implement, the Bayesian dose-escalation design.

RESULTS

MoDEsT comes in two parts: a 'Design' module to explore design options and simulate their operating characteristics, and a 'Conduct' module to guide the dose-finding process throughout the study. We illustrate the practical use of both modules with data from a real phase I study in terminal cancer.

CONCLUSION

Enabling both methodologists and clinicians to understand and apply model-based study designs with ease is a key factor towards their routine use in early-phase studies. We hope that MoDEsT will enable incorporation of Bayesian decision procedures for dose escalation at the earliest stage of clinical trial design, thus increasing their use in early-phase trials.